{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 3: Root Water Uptake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook represents steps to replicate example 3 from: *0David Rassam, Jirka Šimůnek, Dirk Mallants,and Martinus Th. van Genuchten, The HYDRUS-1D Software Package for Simulating the One-Dimensional Movement of Water, Heat, and Multiple Solutes in Variably-Saturated Media* \\\n",
    "Tutorial \\\n",
    "Version 1.00, July 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example provides insctructions to create a Pydrus model that simulating water flow and root water uptake in a 3-m deep uniform soil profile. The soil profile is initially at hydrpstatic equilibrium, i.e., the pressure head changes uniformly from -300 cm at the top of the soil profile to 0 cm at the bottom. Roots are uniformly distributed from the surface down to a depth of 200 cm, which is still 100 cm above the groundwater table. \\\n",
    "The upper boundary and lower boundary are represented with: \n",
    "\n",
    "* Upper BC: Atmospheric Boundary Condition with Surface Runoff.\n",
    "* Bottom BC: Constant Head Boundary (0-cm constant pressure head representing the groundwater table.). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import the Pydrus package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import pydrus as ps\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Create the basic model & add time information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8, 10, 15, 30]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Folder for Hydrus files to be stored\n",
    "ws = \"example_3\"\n",
    "# Path to folder containing hydrus.exe \n",
    "exe = os.path.join(os.getcwd(),\"../../source/hydrus.exe\")  \n",
    "# Description\n",
    "desc = \"Root water uptake in a soil profile\"\n",
    "# Create model\n",
    "ml = ps.Model(exe_name=exe, ws_name=ws, name=\"model\", description=desc, \n",
    "              mass_units=\"mmol\", time_unit=\"days\", length_unit=\"cm\")\n",
    "ml.basic_info[\"lFlux\"] = True\n",
    "ml.basic_info[\"lShort\"] = False\n",
    "\n",
    "time = [1, 2, 3, 4, 5, 6, 7, 8, 10, 15, 30]\n",
    "ml.add_time_info(tmax=30, print_array=time, dt=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Add processes and materials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">water</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>thr</th>\n",
       "      <th>ths</th>\n",
       "      <th>Alfa</th>\n",
       "      <th>n</th>\n",
       "      <th>Ks</th>\n",
       "      <th>l</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.068</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.09</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   water                             \n",
       "     thr   ths   Alfa     n   Ks    l\n",
       "1  0.068  0.38  0.008  1.09  4.8  0.5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.add_waterflow(model=3, top_bc=3, bot_bc=0)\n",
    "\n",
    "m = ml.get_empty_material_df(n=1)\n",
    "m.loc[[1, 1]] = [[0.068, 0.38, 0.008, 1.09, 4.8, 0.5]]\n",
    "ml.add_material(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Add profile information(density to be implemented in ps.create_profile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "nodes = 120  # Disctretize soil column into n elements\n",
    "depth = -300  # Depth of the soil column\n",
    "ihead = -100  # Determine initial Pressure Head\n",
    "# Create Profile\n",
    "profile = ps.create_profile(bot=depth, dx=abs(depth / (nodes-1)), h=ihead)\n",
    "# Discretize profile based on node density\n",
    "tld=0.1  # Lower denstiy at top node\n",
    "bud=0.3  # Upper density bottom node\n",
    "def det_h(nodes,depth, tld=1, lud=1):\n",
    "    midnode = int((nodes*(lud+1)/2+((1+tld)/2))/((1+lud)/2+(1+tld)/2))\n",
    "    \n",
    "    def get_dx(tld, lud, nnodes, depth):\n",
    "        startdx=depth*2/(nnodes-1)/((1+lud/tld))\n",
    "        enddx = depth/(nnodes-1)*lud/((tld+lud)/2)\n",
    "        return np.linspace(startdx,enddx,nnodes-1)\n",
    "    \n",
    "    dx1 = get_dx(tld, 1, midnode, depth/2)\n",
    "    dx2 = get_dx(1, lud, nodes-midnode+1, depth/2)\n",
    "            \n",
    "    values1=[0]\n",
    "    value0=0\n",
    "    for x in dx1:\n",
    "        y = value0+x\n",
    "        value0=y\n",
    "        values1.append(y)\n",
    "    \n",
    "    values2=[]\n",
    "    value0=depth/2\n",
    "    for x in dx2:\n",
    "        y = value0+x\n",
    "        value0=y\n",
    "        values2.append(y)\n",
    "\n",
    "    profile = np.concatenate((values1, values2), axis=None)\n",
    "    return profile\n",
    "profile[\"x\"] = det_h(nodes,depth, tld, bud)\n",
    "profile[\"h\"] = np.round(abs(profile[\"x\"])+depth,5)\n",
    "profile.loc[2:78,\"Beta\"] = 1\n",
    "ml.add_profile(profile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Add observation nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add observation nodes at depth\n",
    "ml.add_obs_nodes([0, -300])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Add atmosphere boundary conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bc = {\"tAtm\": (3, 30), \"Prec\": (2, 0), \"rSoil\": (0.1, 0.1), \"rRoot\": (0.5, 1.5)}\n",
    "atm = pd.DataFrame(bc, index=(3, 30))\n",
    "ml.add_atmospheric_bc(atm, hcrits=0, hcrita=50000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Add root water uptake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml.add_root_uptake(model=0, crootmax=1, poptm=[-25], omegac=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Write hydrus input files and run hydrus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CompletedProcess(args=['C:\\\\Matevz_arbeit\\\\pydrus\\\\examples\\\\hydrus_orig\\\\../../source/hydrus.exe', 'example_3', '-1'], returncode=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.write_input()\n",
    "ml.simulate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "dfs = ml.read_obs_node()\n",
    "figx, ax=plt.subplots()\n",
    "for key, df in dfs.items():\n",
    "    dft = df[:][0:8]\n",
    "    dft.plot(y=\"h\", use_index=True,ax=ax, label=key)\n",
    "    ax.set_ylim(-500,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23be35655f8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.profile_information(times=[1, 3, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23be35d7898>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.profile_information(\"Root Uptake\", times=[1, 3, 6, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23be368eeb8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.profile_information(\"Water Flux\", times=[1, 3, 5, 30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23be36f5048>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.profile_information(\"Pressure Head\", times=[30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000023BE36D4F28>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x0000023BE4742710>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.water_flow(data=\"Actual Surface Flux\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000023BE47BE630>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x0000023BE47EA048>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AjcCHwHpgvqp+JyIPisi5vt0eBToCr4tIpogsauDjIsKsL7eyKquQP50/jO6dbIZbY0x4yi2u4LdzVnJUlwQeDdLkgy3haEddVX0PeM9v2311lie1elAhsnFfEf/4aBNn+Wa8NcaYcOSpUW6dm8mB0koWXn9S0CYfbAkb5K0V1FbtdYyP5cHz2t5sm8aYyPHEJ5tYsjmXB889PqiTD7aEDXXUCp75Yiurswp56tIxdOtoVXvGmPC0eMN+/vnpJn45JoUpY/s2/YYQsxJUiG3YW8Q/Pt7I2SN6c/aI3k6HYyKQrxGRMSG1u6CMW+dlckzPTvy/MGnEZQkqxP7fu+voFO/mwXOPdzoUEwFEZLGIpNZZH4e3xasxIVNR7eGGOSuo9ihPX3YCCXGhHQQ2UFbFF0Jrdxfy5aZc7pp8LMlWtWcC8xDwgYj8D96RVc4Efu1sSCba/eXd9azaVcD/XTaGAd06OB3OIZagQuiZL7bSsV0sl46vf/ZSY/yp6ocich3wEZALjFbVvQ6HZaLYolV7eOmbHVz9owFMHhZetyGsii9Edh0o5d3Ve5g2vp+jzTRNZBGRe4F/AhOB+4HFInK2o0GZqLV5fxF3v7GatP5J3HXmsU6HcxgrQYXIv77ciitGuPJHNgGcaZZuwDhVLQO+EZEPgGeBd50Ny0Sbkopqrpu9ggS3iycvHYO7FQeBDZQlqBA4UFLJ/PRdXDC6j03bbppFVW/xW9+Bd840Y4JGVZm5cA1bc4p55arx9Oocnv+nLEGFwFsrd1NeVcNVP7I5nkzziMhn1DOqv6qe7kA4Jkq9snQHi1btYcYZQzj56G5Oh9MgS1Ah8ObK3Qzrk8gxvTo5HYqJPDPqLMcDvwSqHYrFRKGVO/P507/XcfqxPbjhx+E93U+bTVCV1TV4ajTo7f037y9ize5C7v350KB+rmkbVDXDb9NXIvK5I8GYqHOgpJLfzllBz8R4Hr94JDExznfGbUybS1DlVR6e+GQTL3+9ndIqD1f/aAC/P+u4oPWaXrhiN64Y4dyRNiCsaT4RqTvpTgxwAtDLoXBMFPHUKLfOyyS3uJIF10+gS/vwH6CkTSWorTnF/OaVDDbtL+YcXwL515fbOKF/VyYPO/L/ATU1ytuZezhlcDebTsO0VAbee1CCt2pvG3CVoxGZqPDkp5v5YmMOf75gGCNSujgdTkDaTILK2HGAK19MxxUjvHzlOCYO6U61p4bMXfnM+mJLUBLUt9sPsLugjDsnHxOEiE1bpKrWL8EE3ecbc/jHJxv5xeg+XDoucgYOaBMJaunWPH79wnJ6Jrbj5SvH0y+5PQCxrhguPqEvf/toI4WlVXRuf2Qdat9csZsOcS7OGGo1MqZ5ROQXjb2uqgtbKxYTXXYXlHHr3JUM6dGJP18wPCwGgQ1U1Ceo5dsP8OsXlpOSlMCca8bTo9MP2/ufkJoEwIpd+Zx2TI8WH8dTo3y4bi9nHN8rbAZaNBHlnEZeU8ASlGm22kFgqzzK05eNibj/TY4mKBGZDDwBuIBnVfVhv9fbAS/jvVGcB0xR1e2Bfn7mrgJ+/cJyeneJrzc5AYxM6YIrRli548gS1NrdhRSUVvHjY7q3+DNM26WqNiCsCbo/1xkEdmD3jk6H02xNjm0hIn8Skdg664ki8sKRHlhEXMBTeEdrHgpcIiL+bbOvAvJV9Wjg78BfA/38zF0FXP7cMrp2iOPVq0+sNzkBdGgXy+AeHfluz8EWnUetLzflAPCjMO70ZsKXiLxYZ3m6g6GYKPF25m5e/mYH15wSfoPABiqQwZdigWUiMkJEzsA7N41/X42WGAdsVtWtqloJzAXO89vnPOAl3/IC4CfSRAVqUXk1c5bt4JJZS+nSPo5Xr2l6GI+UpPbsLihr2Vn4fLEpl2F9Em1aDdNSI+ss39LgXsYEYOO+Iu5+Yw3jUrty5+TwGwQ2UE1W8anqTBH5BFgG5AMTVXVzEI7dB9hVZz0LGN/QPqpaLSKFQDLeaQjqtT2vhHveXMu4AV158tLRDZacfnCQLvEs25bX3PgPKa6oZsWOfK6ZaEMbmRY7bHgjY1qiqLyK62Zn0KFdLE9eOjosB4ENVJMJSkQm4r1P9CAwHHhSRK5U1T1HeOz6SkL+F2kg+yAi1wLXAvRMSeWN6ycwpl9SwK1VjuqSQFF5NQfLq0iMb35LvqVb8qiuUU6x6j3Tcim+SQqlzvIhqnqzM2GZSKKq3PXGanbklTL7qvH0iPDBqgNpJPEYcJGqroNDzWE/BY603JgF9K2zngL4J73afbJ898E6Awf8P0hVZwGzANLS0vSE/l39d2lUn6QEALILykns1fwE9eWmHOLdMYdaBBrTAnfUWU5vyQf47uumA7tV9edBicpElOeWbOO9NXuZeeaxTBiU7HQ4RyyQBDVBVT21K6q6MEhjgy0HBovIAGA3MBW41G+fRcB04BvgQuBTVQ16VchRXbwJandBaYsGeP1ycy4nDkymXWxkNeE04UNVX2p6rybdAqwHEoPwWSbCfLvtAA+9/z0/O74n10bJ7YZAEtQ9DVSVPXgkB/bdU7oR+BBvM/PnVfU7EXkQSFfVRcBzwCsishlvyWnqkRyzIX18CWpPQXmz35uVX8rWnBKmje8f7LCMCZiIpABnA38Gbnc4HNPK9heV89tXV9Cva3sevWhkRHXGbUwgCaqkznI88HO839KOmKq+B7znt+2+OsvlwEXBOFZjuvhGkCgorWz2e5ds8rbXmDjY7j8ZR/0DuBOwOV7amGpPDTe+upKi8ipeuWpci+6jh6tAWvH9re66iDyGt+otarSLdRHvjuFgefOn3VmxM5+k9m6O7hF5neBMdBCRnwP7VTVDRH7cyH6HGhP16xc547GZxj364Qa+3XaAv08ZybG9oqt2tyUjSbQHoqOCs47OCW4KS6ua/b7VWYWMSOkSNUVq4yzfPdmbgFTqXJ+qem4jbzsZOFdEzsJby5EoIrNV9bK6O/k3Jgpy6MYBH6zN5pkvtnLZif24YHSK0+EEXSDNzNfw36bdLqA7R3j/KRx1TnBTWNa8BFVe5WHT/mJ+OrRniKIybdBbeO+9vgPUBPIGVZ0JzATwlaBm+CcnE3225BQz4/XVjOzbJWonSA2kBFW3uWo1sE9Vo24K6sT45ieoddkH8dQow/t0DlFUpg0qV9X/aXo305aVVlZz/ewM3C7hf6eNidoWxA0mqDozexb5vZQoIqjqYf2RIlnnBDd7DzavFd+arEKAiJn8y0SEJ0Tkj8B/gIrajaq6IpA3q+piYHFIIjNhQVW5+401bNpfzMtXjjvUCjkaNVaCqjuzpz8lyu5DdU5ws2Gffy5u3OqsQrp1bEfPRBt/zwTNcOBXwOn8t4pPfevG8NLX21m0ag8zzhjCKYOje/aEBhNUW5vZM7EF96DW7C5gREpnayBhgukCYKBvAGVjfiBjxwH+37vrmXRcD2748dFOhxNyDY4i6OtEW7t8fOuE45zEBDfFFdXU1ATWuKmkoprN+4vt/pMJtlWA1Rmbw+QUVXDDnBX0SUrgbxePIiYm+r8YNzbM7ZV1ll8JdSBO65zgRtU7XUcg1mUfpEZhRIolKBNUPYHvReRDEVlU+3A6KOOsak8NN722goLSKp6edgKdE6KnM25jAu0HFfWpuvYXXlhWRef2Tf/yV/saSFgJygTZH50OwISfRz/cwNKtB/jbRSMZelR0dcZtTGMJqouIXIC3lJXoG8X8EFVdGNLIWlnHdt4fRVFFYPeh1mQV0CsxPuKHszfhRVU/F5GewFjfpm9Vdb+TMRlnvb/G2xn3Vyf255cnRF9n3MY0lqA+B2p7r38BnFPnNQWiKkElxHn7EZRXBdQ3ktW7Cxlu1XsmyETkYuBRvE3FBfiniNyhqgscDcw4YvP+Yma8vorR/aK3M25jGmvF9+vWDMRp8bHe23HlVZ4m9vTOoLs1p4TzR/UJdVim7bkHGFtbahKR7sDHgCWoNqa4oprrZmcQ73bxv9PGEBcbuTPjtlRLxuKLSv8tQTWdoLbmFAO0aO4oY5oQ41ell0fjjZlMFFJV7lywiq05xcy+ejy9O0dvZ9zGWILySXB7E1RZAAlqW653BpIB3TqENCbTJn0gIh8Cr/nWpwDvOxiPccCzX/53ZtyTBrXdqXwsQfnE1yaoysASlAj069o+1GGZNkZV7/A1SPoR3ntQs1T1TYfDMq3omy15PPzB90w+vlfUzIzbUoGMZu7CO1NnKj8c/v/x0IXV+moTVHl1040ktueWcFTnhEPvMSZYROSvqnoXdRoh1dlmolx2YRk3vrqC1OT2PHZx9MyM21KB1G2/A1wBJOOdrbP2EVUO3YMKpASVV2rVeyZUflrPtjNbPQrT6iqqPVw/ewXlVR6e+VXaoa4vbVkgP4EUVR0RzIP6Rkqfh7dUth24WFXz/fYZBTwNJAIe4M+qOi+YcdRV24qvqXtQqsq2nGLOHXVUqEIxbZCIXA/cAAwSkdV1XuoEfO1MVKY1PfDOOjJ3FfD0tDE2Q7dPICWo90XkjCAf927gE1UdDHziW/dXClyuqscDk4F/iEjIxiiLdcXgdkmTCSq/tIqD5dWkJlsJygTVq3j7Gr7te659nKCq05wMzITe/PRdvLpsJ9edOogzh/d2OpywEUiCWgq8KSJlInJQRIpE5OARHvc84CXf8kvA+f47qOpGVd3kW94D7Mc7m2/IxLtdTTYzr23BN7C7JSgTPKpaqKrbgWpV3VHncUBEon4szLZsTVYhf3hrLScfncyMM4Y4HU5YCSRB/Q2YALRX1URV7aSqRzoYVE9VzQbwPfdobGcRGQfEAVsaeP1aEUkXkfScnJwWB5XQjARlJSgTIj+YOUBEYoETHIrFhNiBkkqum51Btw5x/M/U0cS6rMtbXYHcg9oErFXVwOah8BGRj4Fe9bx0TzM/pzfe0dSnq2q9TexUdRYwCyAtLa1ZcdYV73Y12cx8e24JrhihrzUxN0EkIjOB3wMJvhqK2uZblfj+tk10qfbUcPNrK8kpruD130wguaNNfOovkASVDSwWkff54RTUjTYzV9VJDb0mIvtEpLeqZvsSUL2DYYpIIvAu8AdVXRpArEckwe1q8h7UtrwSUpIScNs3HRNEqvoQ8JCIPKSqM52Ox4Teo//ZwJLNuTxy4QhG9rUpwOoTSILa5nvE+R7BsAiYDjzse37bfwcRiQPeBF5W1deDdNxGxce5mhwsdntuiTUxNyGjqjNF5Fxgom/TYlX9t5MxmeB7d3U2z3y+lWnj+3FxWl+nwwlbTSYoVX0AQEQ6eVe1OAjHfRiYLyJXATuBi3zHSAOuU9WrgYvxXqTJInKF731XqGpmEI5fr/jYmEZLUKrKttwSxqZ2DVUIpo0TkYeAccAc36ZbRORkK1VFj437irhjwSrG9OvCH8+J+snKj0ggI0kMw3sPqKtvPRdv8+/vWnpQVc0DflLP9nTgat/ybGB2S4/REglxLg6UVDb4el5JJaWVHvon2/0nEzJnA6Nq77eKyEvASsASVBQoLKviN69k0KFdLE9fdkKbHKG8OQL56cwCblfV/qraH/gd8K/QhuWMplrxZReUA3BUl7Y5srBpNXVvSNikY1Gipka5fV4muw6U8r/TxtDTJjttUiD3oDqo6me1K6q6WESi8iZMfBONJLILywDo3dn+sEzIPASsFJHP8Lbkm4iVnqLC/3y6iU++388D5x5vtwkCFEiC2ioi9+Kt5gO4DG+jiajjbWbecCOJvQe9JahelqBMiKjqayKyGO+U7wLcpap7nY3KHKmP1u3jHx9v4pdjUrh8Qn+nw4kYgVTxXYl3BIeFvkc3vIPHRp12sTFUVDdWgirH7RK6dbD+Cib4RCRWRMTXeX0l4AZs3JsIt3l/MbfNy2RESmf+fMGwNj9CeXMEkqAmqerNqjrG97iV+kdcjnhxsTFUNjLdxt7CcnomxhMTY39gJrhE5Bq8/QF3+JY/AS4E5oqITbURoYrKq7j2lXTaxcbwf5edYFP0NFMgVXwzAf9+SPVti3hxrhiqPA0nqOzCMrv/ZELlVmAQ3tHL1wP9VTVXRNoDy4G/Ohmcab6aGuW2eavYmVfK7KvHW+OqFmgwQYnImcBZQB8R+Z86LyXIm5DhAAAZLUlEQVQC1aEOzAlxsTHUqHcIkvrGxMouLGdEivX4NiFR6ZtyJl9ENqtqLoCqlopIw30fTNj656eb+Xj9Pu4/ZygnDkx2OpyI1FgJag+QDpwLZNTZXgTcFsqgnFLbJ6GyngSlqmQXlvOz460EZUIiQURG4612j/Mti+9hf3QR5uN1+/j7xxv55ZgUpp+U6nQ4EavBBKWqq4BVItJTVV+q+5qI3AI8EergWlucLylVVtfQ3m9Qp/zSKiqra+hlfRdMaGQDteNb7q2zXLveIBHpC7yMd3DmGmCWqkbd9RkpNu8v4tZ5mQzvY40ijlQg96CmAo/4bbuCaExQsf9NUP6sD5QJJVU97QjeXg38TlVX+IYkyxCRj1R1XZDCMwEqLKvimpcziHfH8MyvrFHEkWrsHtQlwKXAABFZVOelTkBeqANzQt0qPn97C60PlAlPvmbptfOrFYnIeqAPYAmqFXlqlFvmriQrv5RXrznRGkUEQWMlqK/x/tF3wztpYa0iYHUog3JK3So+f9m+BNW7s/3RmfAlIqnAaGBZPa9dC1wL0K9fv1aNqy149MMNLN6Qw58vGGYjRQRJg/2gfNNNL1bVCcD3eEtOnYAsVY3aVnxQfwkqu7AMV4zQvZN10jXhSUQ6Am8At6rqQf/XVXWWqqapalr37t1bP8AotmjVHv7v8y1cOr4f08bbSBHB0mRHXRG5CPgW75QYFwPLROTCUAfmhKZKUD07tcNlnXRNGBIRN97kNEdVFzodT1uydnchdy5YxdjUJO636TOCKpBGEn8AxqrqfgAR6Q58DCwIZWBOqC1B1ddZd29hud1/Mo4QkRWqOqaR1wV4Dljf1EzXJrhyiyv4zSsZJLWP43+n2fQZwRbITzOmNjn55AX4vohT+8dVUU8Jam9hud1/Mo5oLDn5nAz8CjhdRDJ9j7NaIbQ2rbK6hutnZ5BXUsGsX6VZ9X8IBFKC+kBEPgRe861PAd4LXUjOaaiZeW0n3dOO7eFEWKYNEpFE6lyfqnqgoX1VdQneDr2mlagq9761luXb8/nnJaMZnmLTdoVCIFO+3yEivwB+hPcimKWqb4Y8Mgc0dA+qqKKasioPPRPtG5IJLRH5DfAgUAaob7MCAx0Lyhzmpa+3My99FzeedjTnjDzK6XCiViAlKHw3XReKSDeC0AdKRLoC84BUYDtwsW8csvr2TcQ7eOabqnrjkR67MQ214ssr9g6F1q2jJSgTcjOA42vH4jPhZ8mmXP707np+OrQnt/90iNPhRLUG7yWJyIkislhEForIaBFZC6wF9onI5CM87t3AJ6o6GO+0Anc3su+fgM+P8HgBqS1B+TeSyCuuAKBrh7jD3mNMkG0BSp0OwtRvW24Jv311BUd378jfp4yyqXdCrLES1JPA74HOwKfAmaq6VESOxXs/6oMjOO55wI99yy8Bi4HD5rwRkROAnr5jpR3B8QLS0D2ovBIrQZlWMxP4WkSWARW1G1X1ZudCMgAHy6u45uV0YgSenZ5Gx3YBVUCZI9DYTzhWVf8DICIPqupSAFX9PgiDH/b0Dc+CqmaLyGGtD0QkBu8IFr8CftLYhwWrh3yDCcpXxZfc0UpQJuSewfuFcA3egV9NGKj21HDjqyvZnlvC7KvH07dre6dDahMaS1B1L44yv9eUJojIx3hHV/Z3TwBxAdwAvKequ5pKiKo6C5gFkJaW1mRsDWmomblV8ZlWVK2qtzsdhPmh//fuer7YmMNffznc5nZqRY0lqJEichBvy70E3zIEOD+Nqk5q6DUR2ScivX2lp954p7r2NwE4RURuADrinSOnWFUbu191RGrvQVXX/DDH5ZVU0ik+lnaxNjKxCbnPfDUC7/DDKr4Gm5mb0Jq9dAcvfr2dq380gCljbQzD1tTYfFCh/G+8CJgOPOx7frue40+rXRaRK4C0UCYngFjfDc+qeu5BJVvpybSOS33PM+tss2bmDlmyKZc/LvqO04/twcyzjnM6nDbHqbt8DwPzReQqYCfecf4QkTTgOlW92omgXDGCSP2t+JKtgYRpBao6wOkYjNeWnGJumJPB0d078sTUUTYOpwMcSVCqmkc9DR9UNR04LDmp6ovAi6GOS0Rwu2Ko9Pywiu9ASSX97KaoaQUicnl921X15daOpS0rKK3k6pfScbtieHZ6Gp3i3U6H1CZZO0k/ca4Yqv1KULnFlYzul+RQRKaNGVtnOR7vF7kVeKd0N62gsrqG37ySwe78Ml69xlrsOckSlJ9Yl/ygiq+mRskvraSbNTE3rUBVb6q7LiKdgVccCqfNUVXuXriaZdsO8MTUUaTZxIOOispRyY+EfxVfYVkVnhq1JubGKaXAYKeDaCue/HQzC1fs5rZJQzhvVB+nw2nzrATlx7+KL6/E29LXGkmY1iAi7/DffoYxwFBgvnMRtR1vZ+7mbx9t5Bej+3DzT452OhyDJajD+Ffx5dYOFGslKNM6HquzXA3sUNUsp4JpK9K3H+CO11czbkBXHvrlcIIwWo4JAktQftyuGKrqVPHVDnPU1e5BmRASkaPxDgH2ud/2U0SknapucSi0qLcjr4RrX8mgT1ICz1x2gnXIDyN2D8qP9x7Uf0tQ+aW+BNXeEpQJqX8ARfVsL/O9ZkLgQEklV7ywnBpVnr9iLElWUxJWLEH5cbvkB/eg8n0jmXexBGVCK1VVV/tv9PUNTG39cKJfWaWHq15azp6CMp6bnsaAbh2cDsn4sSo+P/5VfPmlVXRsF3toIFljQqSx8S0TWi2KNsJTo9wydyWZuwp4etoJnNDfmpOHI/uv68ftkh9U8RWUVtKlvfUiNyG3XESu8d/oGw4sw4F4opaqct/ba/nPun388edDmTysvkkXTDiwEpQftyuG4orqQ+sHSitJsuo9E3q3Am+KyDT+m5DSgDjgAseiikJPfrqZOct2cv2PB3HFyTb0YTizBOXH7Yqh2q+Kz26cmlBT1X3ASSJyGjDMt/ldVf3UwbCiztxvd3r7Oo3pw50/O8bpcEwTLEH5cfv1gyoorSQ12cbiMq1DVT8DPnM6jmj0wdq9/P7NNZw6pDt//eUI6+sUAewelJ/DmpmXWBWfMZHu6y253PzaSkb27cLTl43B7bJ/fZHAfkt+vK34vAmq2lPDwfJqayRhTARbk1XItS9nkNqtPS9cMZb2cVZxFCksQfnx9oPy3oMqKKsCsIFijYlQG/cVcfnzy+ic4OblK8dbf8YIYwnKT90SVEGpddI1JlLtyCvhsmeX4XbF8Oo14+nVubGuZiYcOZKgRKSriHwkIpt8z/XOBigi/UTkPyKyXkTWiUhqqGNzu2KorPYmqAMl3hJUklXxGRNR9hSUMe3ZZVR6aph99Xj6J9soEZHIqRLU3cAnqjoY+MS3Xp+XgUdV9ThgHLA/1IF5W/F5q/hqx+GzRhLGRI69heVc8q+lFJZW8fKV4xjSs5PTIZkWcipBnQe85Ft+CTjffwcRGQrEqupHAKparKqloQ4sLjaGimoPqnqois/6QZlwJyKTRWSDiGwWkYa+8EW9/UXlXPrsUnKLKnjpqnGMSOnidEjmCDjVnKWnqmYDqGq2iPSoZ58hQIGILAQGAB8Dd6uqx39HEbkWuBagX79+RxRYgttFjUKVR62Kz0QEEXEBTwE/BbLwDpu0SFXXHcnn5pdUkplVwKpdBWzYW0R+aSVHdUlg+oRURvYNv3/8ecUVTPvXMrILynn5qnGM6VfvnQMTQUKWoETkY6C+Qa7uCfAjYoFTgNHATmAecAXwnP+OqjoLmAWQlpam/q83R7zbOxdMebWHgtJK4mJjSHDb/DAmrI0DNqvqVgARmYu3lqLBBFXlUfYUlFHlqaG4opr8kiqyC8vYkVfKpv1FfLfnIFn5ZXg/D1KTO9CtYxyfrN/Pv1dl86/paZw6pHtrnFtA8ksqmfbsMnbll/LCFeMYm2qDv0aDkCUoVZ3U0Gsisk9EevtKT72p/95SFrCyzkX3FnAi9SSoYDqUoCo95JdWktTebT3OTbjrA+yqs54FjG/sDd/vPchJDx8+ilKMLxmN7NuFy07sz8iULoxI6UyHdt5/FYWlVUyZ9Q2/m7+Kz2acSqd452sXCkor+dXzy9iaW8Jz09OYMCjZ6ZBMkDhVxbcImA487Ht+u559lgNJItJdVXOA04H0UAdWW1oqr6rhgI0iEbCqqiqysrIoLy93OpSQio+PJyUlBbfb+X/MddT3DeqwmoS6VeHdUwbw0C+GE+eKoUM7F13ax9ErMZ6juiQ0OrVM5/ZuHv7lCM5/6ivmfruLayYODNpJtMT+onIuf+5btuaU8MyvTuCUweFTqjNHzqkE9TAw3zeVwE7gIgARSQOuU9WrVdUjIjOAT8RbhMkA/hXqwGpLUGVVHvJKKunWsV2oDxkVsrKy6NSpE6mpqVFb4lRV8vLyyMrKYsCAsBoFOwvoW2c9Bdjjv5N/Vfgl41p2v3ZU3y6MTU3itW93cvUpAxz7fe8uKOOyZ5ext7Cc568Yy48Gd3MkDhM6jrTiU9U8Vf2Jqg72PR/wbU9X1avr7PeRqo5Q1eGqeoWqVoY6toQ474+kvMpDXnElyR2tBBWI8vJykpOTozY5AYgIycnJ4VhKXA4MFpEBIhIHTMVbSxEyF56QwtbcElbuKgjlYRq0LbeEi57+mtziCmZfPc6SU5SykST8xMfWKUEVV5DcwUpQgYrm5FQrHM9RVauBG4EPgfXAfFX9LpTHPGt4b+LdMbyRkRXKw9RrffZBLvq/byivruG1a0602XCjmCUoP/Fx3gRVUFpFSaXHSlAmIqjqe6o6RFUHqeqfQ328TvFufnZ8L95ZtYeK6sN6foRMxo58ps5aSmyMMP83ExjWp3OrHdu0PktQfmpLULsLvE1sk62TbsTavn07CQkJjBo1ipEjR3LSSSexYcOGRt+TmZnJe++910oRRraLTujLwfJq3l2d3SrHe2vlbi7511K6tHfz+nUTOLpHx1Y5rnGOJSg/Cb4S1G5fH5BkayQR0QYNGkRmZiarVq1i+vTp/OUvf2l0f0tQgTv56GSO7tGR57/ahuoRdT9sVE2N8tiHG7h1Xiaj+3bhrRtOpm9Xm0S0LbCJUfzEu705e3eBd1Qlq+Jrvgfe+Y51ew4G9TOHHpXIH885vsHX77rrLvr3788NN9wAwP3330+nTj8cg+3gwYMkJXlHFygvL+f6668nPT2d2NhYHn/8cU4++WTuu+8+ysrKWLJkCTNnzmTKlClBPY9oIiL8+uRU7nlzLcu35zNuQPDvBZVVevjd65m8t2YvU8f25cHzhjXaDN5EF/tN+6ntB2VVfJFl6tSpzJs379D6/PnzGTt2LFu2bGHUqFEMGjSIxx9/nNtvvx2Ap556CoA1a9bw2muvMX36dGpqanjwwQeZMmUKmZmZlpwC8IvRKXTtEMffP9oY9FLU3sJyLn7mG95fu5c/nH2ct9+WJac2xUpQfmr7QVkVX8s1VtIJldGjR7N//3727NlDTk4OSUlJ9OvX71AVH8C8efO49tpr+eCDD1iyZAk33XQTAMceeyz9+/dn48aNrR53pEuIc3HrpMHc9/Z3fLJ+P5OG9gzK536wNpuZC9dQWV3Ds5en8ZPjgvO5JrLY1xE/7WJjEIH80iraxcbQIc7G4YsUF154IQsWLGDevHlMnTr1sNfPPfdcvvjiC4CQ3jNpay4Z14+B3Tvw4L/XUVxRfUSfdbC8itvnZ3Ld7BWkJLXn7RtPtuTUhlmC8iMih1ryJXeIC8t+L6Z+U6dOZe7cuSxYsIALL7zwsNeXLFnCoEGDAJg4cSJz5swBYOPGjezcuZNjjjmGTp06UVRU1KpxRzq3K4aHfzGCrPxS7n1rbYuT/9ebc5n89y94O3MPN59+NAtvOImje9hcTm2ZJah61DaUsOq9yHL88cdTVFREnz596N27N8Che1AjR47k97//Pc8++ywAN9xwAx6Ph+HDhzNlyhRefPFF2rVrx2mnnca6desYNWrUD+5pmcaNG9CVm38ymDdX7ubet9dS7akJ+L37D5Zz39trufTZZcS7XSy4bgK3n3EMbpf9e2rr7B5UPRLcLvKpshZ8EWjNmjWHllNTUykrK6t3v/j4eF588cXDtnft2pXly5eHKryodvPpgymvquH/Pt9CVn4ZfzpvWIPNwVWV9dlFzF62gwXpWVTX1DB9Qn/uPvO4Q109jLEEVY/aC6S9XSjGBCwmRrj7zGNJSUrgwX+v47THFvPzEb0Z1qczA7p1QAR2F5SzNaeYj9fvY9eBMuJcMVyYlsJvJg6kf3IHp0/BhBlLUPW47tRB3LFgNQO62QVjTHNddmJ/Jh3Xk6cXb+atzD28lfnDgdXjYmM4eVAyv/3x0fx0aE+rSjcNsgRVj4vS+jKmfxK9EuOdDiWiqGrUNyqx1n+B6dU5ngfOG8b95x5PfmkV23JLAEhJSqBbx3a4YqL778QEhyWoBgzqbuN8NUd8fDx5eXlRPeVG7XxQ8fH2xSVQIkLXDnF0tQ7vpgUsQZmgSElJISsri5ycHKdDCanaGXWNMaFnCcoEhdvtDrdZZo0xEc46GhhjjAlLlqCMMcaEJUtQxhhjwpJEW7NZESkCGp82NfJ0A3KdDiKIou18AI5R1YgZOM6uk4gQbefT7GskGhtJbFDVNKeDCCYRSY+mc4q28wHvOTkdQzPZdRLmovF8mvseq+IzxhgTlixBGWOMCUvRmKBmOR1ACETbOUXb+UDknVOkxRuIaDunNn8+UddIwhhjTHSIxhKUMcaYKGAJyhhjTFiKqgQlIpNFZIOIbBaRu52OpyVE5HkR2S8ia+ts6yoiH4nIJt9zkpMxNoeI9BWRz0RkvYh8JyK3+LZH5DmJSLyIfCsiq3zn84Bv+wARWeY7n3kiErbDd0f6dWLXSPgL1nUSNQlKRFzAU8CZwFDgEhEZ6mxULfIiMNlv293AJ6o6GPjEtx4pqoHfqepxwInAb32/l0g9pwrgdFUdCYwCJovIicBfgb/7zicfuMrBGBsUJdfJi9g1Eu6Ccp1ETYICxgGbVXWrqlYCc4HzHI6p2VT1C+CA3+bzgJd8yy8B57dqUEdAVbNVdYVvuQhYD/QhQs9JvYp9q27fQ4HTgQW+7eF8PhF/ndg1Ev6CdZ1EU4LqA+yqs57l2xYNeqpqNnj/mIEeDsfTIiKSCowGlhHB5yQiLhHJBPYDHwFbgAJVrfbtEs5/e9F6nUTs31Nd0XKNQHCuk2hKUPVN42pt6MOEiHQE3gBuVdWDTsdzJFTVo6qjgBS8JZLj6tutdaMKmF0nYSqarhEIznUSTQkqC+hbZz0F2ONQLMG2T0R6A/ie9zscT7OIiBvvhTdHVRf6Nkf0OQGoagGwGO99gy4iUju2ZTj/7UXrdRLRf0/Reo3AkV0n0ZSglgODfa1E4oCpwCKHYwqWRcB03/J04G0HY2kWERHgOWC9qj5e56WIPCcR6S4iXXzLCcAkvPcMPgMu9O0WzucTrddJRP49QfRdIxDE60RVo+YBnAVsxFvXeY/T8bTwHF4DsoEqvN92rwKS8bbi2eR77up0nM04nx/hLcavBjJ9j7Mi9ZyAEcBK3/msBe7zbR8IfAtsBl4H2jkdayPnENHXiV0j4f8I1nViQx0ZY4wJS9FUxWeMMSaKWIIyxhgTlixBGWOMCUuWoIwxxoQlS1DGGGPCkiWoMCQiySKS6XvsFZHddda/DsHxrhCRHBF5toHXF4tIWhCP96jvvGYE6zNN22PXSfSLbXoX09pUNQ/vCMCIyP1Asao+FuLDzlPVG0N8DABU9Q4RKWmNY5noZddJ9LMSVIQRkWLf849F5HMRmS8iG0XkYRGZ5puDZY2IDPLt111E3hCR5b7HyQEcI0FE5orIahGZByTUee1pEUn3m+PlJyLyZp19fioiC32DRb4oImt9Md0W9B+IMfWw6yQ6WAkqso3EOwDjAWAr8KyqjhPvhGc3AbcCT+Cdf2WJiPQDPqT+QRvruh4oVdURIjICWFHntXtU9YB45xX6xPf6p8BTItJdVXOAXwMv4P1220dVhwHUDn1iTCuz6yRCWQkqsi1X71wyFXiHrfmPb/saINW3PAl4UrzD3i8CEkWkUxOfOxGYDaCqq/EOV1LrYhFZgXcYk+OBoeodjuQV4DLfxTUBeB/vP4OBIvJPEZkMRPwIzSYi2XUSoawEFdkq6izX1Fmv4b+/2xhggqqWNfOzDxsDS0QGADOAsaqaLyIvAvG+l18A3gHKgdfVO+dLvoiMBH4G/Ba4GLiymXEYc6TsOolQVoKKfv8BDt3UFZFRAbznC2Cab/9heAd+BEgESoBCEemJd9pwAFR1D96h8/+Ad0puRKQbEKOqbwD3AmOO8FyMCRW7TsKQlaCi3814671X4/19fwFc18R7ngZe8L0nE+/ow6jqKhFZCXyHt1riK7/3zQG6q+o633of3+fUfhGaeaQnY0yI2HUShmw0c4OIXAGkHWnzWRF5Elipqs8FsO/9tE6zYGOCwq6T1mdVfAagDDhTGuiAGAgRycBbxTE7gH0fBS7DWw1iTKSw66SVWQnKGGNMWLISlDHGmLBkCcoYY0xYsgRljDEmLFmCMsYYE5YsQRljjAlL/x/khrRZq+woMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.water_flow(data=\"Bottom Flux\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000023BE4866978>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x0000023BE48895C0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.water_flow(\"Actual Root Water Uptake\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
